Srinivas Devadas

Bio: Srinivas Devadas is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Sequential logic & Combinational logic. The author has an hindex of 88, co-authored 480 publications receiving 31897 citations. Previous affiliations of Srinivas Devadas include University of California, Berkeley & Cornell University.

A low-overhead dynamic optimization framework for multicores

Abstract: This paper argues for a “less is more” design philosophy when integrating dynamic optimization into a multicore system. The primary insight is that dynamic optimization is inherently loosely-coupled and can therefore be supported on multicores with very low-overhead by using a Partner core. We exploit this property by designing a dynamic optimizer composed of a two-core partnership that requires a minimal amount of dedicated hardware and is resilient to (a) reducing the Partner core's clock frequency, (b) changing the Partner core's placement on the multicore die and (c) varying the latency of dynamic optimization operations.

An Architecture to Accelerate Computation on Encrypted Data

Abstract: Fully homomorphic encryption (FHE) allows computing on encrypted data, enabling secure offloading of computation to untrusted servers. Though it provides ideal security, FHE is prohibitively expensive when executed in software. These overheads are a major barrier to FHE's widespread adoption. We present F1, the first FHE accelerator that is capable of executing full FHE programs. F1 builds on an in-depth architectural analysis of the characteristics of FHE computations that reveals acceleration opportunities. F1 is a wide-vector processor with novel functional units deeply specialized to FHE primitives, such as modular arithmetic, number-theoretic transforms, and structured permutations. This organization provides so much compute throughput that data movement becomes the bottleneck. Thus, F1 is primarily designed to minimize data movement. F1 is the first system to accelerate complete FHE programs, and outperforms state-of-the-art software implementations by gmean 5,400x. These speedups counter FHE's overheads and enable new applications, like real-time private deep learning in the cloud.

ShorTor: Improving Tor Network Latency via Multi-hop Overlay Routing

Abstract: We present ShorTor, a protocol for reducing latency on the Tor network. ShorTor uses multi-hop overlay routing, a technique typically employed by content delivery networks, to influence the route Tor traffic takes across the internet. In this way, ShorTor avoids slow paths and improves the experience for end users by reducing the latency of their connections while imposing minimal bandwidth overhead. ShorTor functions as an overlay on top of onion routing—Tor’s existing routing protocol—and is run by Tor relays, making it independent of the path selection performed by Tor clients. As such, ShorTor reduces latency while preserving Tor’s existing security properties. Specifically, the routes taken in ShorTor are in no way correlated to either the Tor user or their destination, including the geographic location of either party. We analyze the security of ShorTor using the AnoA framework, showing that ShorTor maintains all of Tor’s anonymity guarantees. We augment our theoretical claims with an empirical analysis. To evaluate ShorTor’s performance, we collect a real-world dataset of over 400,000 latency measurements between the 1,000 most popular Tor relays, which collectively see the vast majority of Tor traffic. With this data, we identify pairs of relays that could benefit from ShorTor: that is, two relays where introducing an additional intermediate network hop results in lower latency than the direct route between them. We use our measurement dataset to simulate the impact on end users by applying ShorTor to two million Tor circuits chosen according to Tor’s specification. ShorTor reduces the latency for the 99th percentile of relay pairs in Tor by 148ms. Similarly, ShorTor reduces the latency of Tor circuits by 122ms at the 99th percentile. In practice, this translates to ShorTor truncating tail latencies for Tor which has a direct impact on page load times and, consequently, user experience on the Tor browser.

A framework to accelerate sequential programs on homogeneous multicores

Abstract: This paper presents a light-weight dynamic optimization framework for homogeneous multicores Our system profiles applications at runtime to detect hot program paths, and offloads the optimization of these paths to a Partner core Our work contributes two insights: (1) that the dynamic optimization process is highly insensitive to runtime factors in homogeneous multicores and (2) that the Partner core's view of application hot paths can be noisy, allowing the entire optimization process to be implemented with very little dedicated hardware in a multicore

TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones

Abstract: Today’s smartphone operating systems frequently fail to provide users with visibility into how third-party applications collect and share their private data. We address these shortcomings with TaintDroid, an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data. TaintDroid enables realtime analysis by leveraging Android’s virtualized execution environment. TaintDroid incurs only 32p performance overhead on a CPU-bound microbenchmark and imposes negligible overhead on interactive third-party applications. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, in our 2010 study we found 20 applications potentially misused users’ private information; so did a similar fraction of the tested applications in our 2012 study. Monitoring the flow of privacy-sensitive data with TaintDroid provides valuable input for smartphone users and security service firms seeking to identify misbehaving applications.

TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones

Abstract: Today's smartphone operating systems frequently fail to provide users with adequate control over and visibility into how third-party applications use their private data. We address these shortcomings with TaintDroid, an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data. TaintDroid provides realtime analysis by leveraging Android's virtualized execution environment. TaintDroid incurs only 14% performance overhead on a CPU-bound micro-benchmark and imposes negligible overhead on interactive third-party applications. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, we found 68 instances of potential misuse of users' private information across 20 applications. Monitoring sensitive data with TaintDroid provides informed use of third-party applications for phone users and valuable input for smartphone security service firms seeking to identify misbehaving applications.

Symbolic Boolean manipulation with ordered binary-decision diagrams

Abstract: Ordered Binary-Decision Diagrams (OBDDs) represent Boolean functions as directed acyclic graphs. They form a canonical representation, making testing of functional properties such as satisfiability and equivalence straightforward. A number of operations on Boolean functions can be implemented as graph algorithms on OBDD data structures. Using OBDDs, a wide variety of problems can be solved through symbolic analysis. First, the possible variations in system parameters and operating conditions are encoded with Boolean variables. Then the system is evaluated for all variations by a sequence of OBDD operations. Researchers have thus solved a number of problems in digital-system design, finite-state system analysis, artificial intelligence, and mathematical logic. This paper describes the OBDD data structure and surveys a number of applications that have been solved by OBDD-based symbolic analysis.

Physical unclonable functions for device authentication and secret key generation

Abstract: Physical Unclonable Functions (PUFs) are innovative circuit primitives that extract secrets from physical characteristics of integrated circuits (ICs). We present PUF designs that exploit inherent delay characteristics of wires and transistors that differ from chip to chip, and describe how PUFs can enable low-cost authentication of individual ICs and generate volatile secret keys for cryptographic operations.